【作者】 张鹏；

【导师】 李耀翔；

【作者基本信息】 东北林业大学 ， 森林工程， 2014， 硕士

【摘要】 木材的抗压强度、抗弯弹性模量和抗弯强度是评价木材的力学性质的重要性能指标,对木材力学性质的快速预测,能够缓解木材的供需关系,提高木材利用率,并为人工林的合理培育和加工提供依据。本文采用近红外光谱技术结合偏最小二乘方法对落叶松木材的三个力学性质进行预测模型的优化研究,并引入小波变换的方法对近红外光谱进行预处理,去除信号里面的噪音成分。得到结论如下：(1)对落叶松木材抗压强度进行预测。综合分析校正集和验证集两个方面,选择均方根误差达到最小和相关系数达到最高的位置作为最佳主成分个数,心材和边材的最佳主成分个数都为4；分别比较固定阈值法、无偏似然估计阈值法、启发式阈值法、最大最小阈值法对光谱去噪效果的不同,结果表明,启发式阈值对心材模型去噪效果最好,校正集SEC和RMSEC分别为5.7921和5.7631,相关系数R为0.7922,验证集SEP和RMSEP分别为8.0835和8.0799,相关系数R为0.6415；无偏似然估计阈值对边材模型去噪效果最好,校正集SEC和RMSEC分别为5.7832和5.7491,相关系数R达到最高为0.7267；验证集SEP和RMSEP分别为6.5891和6.5362,相关系数R最高为0.6594。(2)对落叶松木材抗弯弹性模量(MOE)进行预测。以落叶松心材样本为例,横切面模型预测效果最好,径切面模型预测效果略次之,弦切面模型效果最低；以落叶松边材样本为例,进行db5-4层次小波分解的去噪效果最好,校正集的SEC和RMSEC达到最小值为0.5970和0.5935,相关系数R达到最大值为0.8562：验证集的SEP和RMSEP达到最小值为0.9720和0.9784,相关系数R达到最大值为0.7547。(3)对落叶松木材抗弯强度(MOR)进行预测。选择db5-7、bior5.5-7、dmey7、coif5-7和rbio5.5-7小波函数进行小波去噪处理,结果表明,对于心材样本,dmey7小波函数取得了最佳的预测效果,校正集的SEC和RMSEC分别为6.4152和6.3774,相关系数R为0.7956；验证集的SEP和RMSEP分别为5.8817和5.9811,相关系数R为0.8408；对于边材样本,db5-7小波建立的模型精度达到最高,校正集的SEC和RMSEC分别为5.7457和5.7147,相关系数R为0.7627;验证集的SEP和RMSEP分别为7.0626和7.0129,相关系数R为0.6929。更多还原

【Abstract】 Compressive strength, modulus of elastic(MOE) and modulus of rupture(MOR) are the key performance indicators to evaluate the mechanical properties of wood. Rapid prediction of the mechanical properties makes significant contribution to ease the contradiction between supply and demand of wood, and to provide reasonable basis for the scientific cultivation and processing of plantation. In this paper, near infrared reflectance spectroscopy (NIRS) coupled with partial least squares regression(PLS) method is investigated to predict the mechanical properties of larch wood. Meanwhile, wavelet transform is introducted to denoise spectral signal. The main conclusion are as follows:(1) Compressive strength predictions for larch wood. Undertaking comprehensive analysis of the calibration and validation sets, we choose the number of best principal components when root mean square error(RMSE) reaches the minimum and the correlation coefficient(R) reaches its maximum value. The number of best principal components are both4with heartwood and sapwood models. Comparison of Sqtwolog, Rigrsure, Heursure and Minimaxi threshold, the result shows that Heursure gained the best effect of de-noising in the heartwood model, the standard error of calibration(SEC) and the root mean square error of calibration (RMSEC) were5.7921and5.7631, the R was0.7922; the standard error of prediction(SEP) and the root mean square error of prediction(RMSEP) were8.0835and8.0799, the R was0.6415. Rigrsure gained the best result in the sapwood model, the SEC and RMSEC were5.7832and5.7491, the R was0.7267; the SEP and RMSEP were6.5891and6.5362, the R was0.6594.(2) MOE predictions for larch wood. As an example of the larch heartwood sample, the best prediction accuracy is the cross-sectional model, the radial section was slightly worse, the string section was the worst. For the sapwood sample, db5-4achieved the best results in the de-noising, the SEC and RMSEC were0.5970and0.5935, the R was0.8562; the SEP and RMSEP were0.9720and0.9784, the R was0.7547.(3) MOR predictions for larch wood. We collected db5-7、bior5.5、dmey7、coif5-7and rbio5.5-7wavelet functions to pretreat spectral signals. According to results, dmey7obtained the best prediction accuracy in the heartwood model, the SEC and RMSEC were6.4152and6.3774, the R was0.7956, the SEP and RMSEP were5.8817and5.9811, the R was0.8408; db5-7acquired the best de-noising affections in the sapwood model, the SEC and RMSEC were5.7457and5.7147, the R was0.7627, the SEP and RMSEP were7.0626and7.0129, the R was0.6929.更多还原